Getting Started with mongolstats

Your First Analysis: Infant Mortality Trends

Let’s walk through a complete workflow using infant mortality data—a key indicator of population health and health system performance.

Step 1: Load Packages

library(mongolstats)
library(dplyr)
library(ggplot2)

# Set language to English for readable output
nso_options(mongolstats.lang = "en")

Step 2: Find the Right Table

Search for infant mortality data:

# Search by keyword
mortality_tables <- nso_itms_search("infant mortality")
mortality_tables |>
    select(tbl_id, tbl_eng_nm) |>
    head(5)
#> # A tibble: 5 × 2
#>   tbl_id            tbl_eng_nm                                                  
#>   <chr>             <chr>                                                       
#> 1 DT_NSO_2100_014V1 NUMBER OF INFANT MORTALITY, aimags and the Capital and by m…
#> 2 DT_NSO_2100_014V2 INFANT MORTALITY RATE, per 1000 live births, aimags and the…
#> 3 DT_NSO_2100_014V4 INFANT MORTALITY, by sex, by soum, and by year              
#> 4 DT_NSO_2100_014V5 INFANT MORTALITY RATE,  per 1000 live births, by sex, by so…
#> 5 DT_NSO_2100_015V1 INFANT MORTALITY RATE, per 1000 live births, aimags and the…

We’ll use DT_NSO_2100_015V1 - Infant Mortality Rate per 1,000 live births (Monthly).

Step 3: Explore Table Metadata

Before fetching data, check what dimensions are available:

# View table structure
meta <- nso_table_meta("DT_NSO_2100_015V1")
meta
#> # A tibble: 2 × 5
#>   dim    code  is_time n_values codes             
#>   <chr>  <chr> <lgl>      <int> <list>            
#> 1 Region Бүс   FALSE         28 <tibble [28 × 3]> 
#> 2 Month  Сар   FALSE        118 <tibble [118 × 3]>

# Check available months
time_vals <- nso_dim_values("DT_NSO_2100_015V1", "Month", labels = "en")
head(time_vals, 10)
#> # A tibble: 10 × 2
#>    code  label_en
#>    <chr> <chr>   
#>  1 0     2025-10 
#>  2 1     2025-09 
#>  3 2     2025-08 
#>  4 3     2016-01 
#>  5 4     2016-02 
#>  6 5     2016-03 
#>  7 6     2016-04 
#>  8 7     2016-05 
#>  9 8     2016-06 
#> 10 9     2016-07

Step 4: Fetch Data

Get national infant mortality rates for the past two decades:

# Get all month codes
months <- nso_dim_values("DT_NSO_2100_015V1", "Month", labels = "en")

imr_national <- nso_data(
    tbl_id = "DT_NSO_2100_015V1",
    selections = list(
        "Region" = "0", # National level
        "Month" = months$code
    ),
    labels = "en" # Get English labels
)

# Preview
imr_national |>
    head(10)
#> # A tibble: 10 × 5
#>    Region Month value Region_en Month_en
#>    <chr>  <chr> <dbl> <chr>     <chr>   
#>  1 0      0        11 Total     2025-10 
#>  2 0      1        14 Total     2025-09 
#>  3 0      2        16 Total     2025-08 
#>  4 0      3        12 Total     2016-01 
#>  5 0      4        13 Total     2016-02 
#>  6 0      5        14 Total     2016-03 
#>  7 0      6        15 Total     2016-04 
#>  8 0      7        15 Total     2016-05 
#>  9 0      8        14 Total     2016-06 
#> 10 0      9        14 Total     2016-07

Step 5: Visualize the Trend

Create a publication-ready plot:

# Prepare the data for visualization
# Step 1: Convert month strings to proper dates for time series plotting
# Step 2: Filter to recent decade (2015-2024) for clear trend visibility

p <- imr_national |>
    mutate(date = as.Date(paste0(Month_en, "-01"))) |> # Convert "YYYY-MM" to date
    filter(date >= as.Date("2015-01-01") & date <= as.Date("2024-12-31")) |>
    ggplot(aes(x = date, y = value,
               group = 1,
               text = paste0("<b>Date:</b> ", format(date, "%Y-%m"), "<br>",
                             "<b>IMR:</b> ", value))) +
    geom_line(color = "#2c3e50", linewidth = 1, alpha = 0.3) +
    geom_point(color = "#e74c3c", size = 3, shape = 21, fill = "white", stroke = 1.5, alpha = 0.6) +
    geom_smooth(method = "loess", se = TRUE, color = "#3498db", fill = "#3498db", alpha = 0.2, linewidth = 1.5) +
    scale_x_date(date_breaks = "1 year", date_labels = "%Y") +
    labs(
        title = "Infant Mortality Rate in Mongolia (Monthly)",
        subtitle = "Deaths per 1,000 live births (National Trend)",
        x = NULL,
        y = "IMR (per 1,000)",
        caption = "Source: NSO Mongolia via mongolstats"
    ) +
    theme_minimal(base_size = 14) +
    theme(
        plot.title = element_text(face = "bold", size = 16),
        plot.subtitle = element_text(color = "grey40"),
        panel.grid.minor = element_blank(),
        panel.grid.major.x = element_blank()
    )

plotly::ggplotly(p, tooltip = "text")

Regional Comparison

Compare infant mortality across different regions:

# Get all aimags for most recent year
# Get all aimags for most recent year (2024)
# We'll take the average of monthly rates
months_2024 <- months |>
    filter(grepl("2024", label_en)) |>
    pull(code)

# Fetch IMR data for all regions in 2024
# We'll calculate the annual average from monthly data

imr_regional <- nso_data(
    tbl_id = "DT_NSO_2100_015V1",
    selections = list(
        "Region" = nso_dim_values("DT_NSO_2100_015V1", "Region")$code,
        "Month" = months_2024
    ),
    labels = "en"
) |>
    filter(nchar(Region) == 3) |> # Keep only Aimags and Ulaanbaatar (code length = 3)
    mutate(
        Region_en = trimws(Region_en),
        # Standardize region names to match geographic boundary data
        Region_en = dplyr::case_match(
            Region_en,
            "Bayan-Ulgii" ~ "Bayan-Ölgii",
            "Uvurkhangai" ~ "Övörkhangai",
            "Khuvsgul" ~ "Hovsgel",
            "Umnugovi" ~ "Ömnögovi",
            "Tuv" ~ "Töv",
            "Sukhbaatar" ~ "Sükhbaatar",
            .default = Region_en
        ),
        Type = ifelse(Region %in% c("1", "2", "3", "4"), "Region", "Aimag")
    ) |>
    # Calculate annual average IMR from monthly data
    group_by(Region_en, Type) |>
    summarise(value = mean(value, na.rm = TRUE), .groups = "drop")

# Top 10 highest IMR regions
imr_regional |>
    arrange(desc(value)) |>
    select(Region_en, value) |>
    head(10)
#> # A tibble: 10 × 2
#>    Region_en    value
#>    <chr>        <dbl>
#>  1 Hovsgel       27.2
#>  2 Arkhangai     24.8
#>  3 Övörkhangai   23.9
#>  4 Bayankhongor  21.6
#>  5 Ömnögovi      19.9
#>  6 Uvs           19.8
#>  7 Sükhbaatar    17.9
#>  8 Bayan-Ölgii   17.7
#>  9 Zavkhan       17.5
#> 10 Khovd         16.8

Visualize Regional Disparities

p <- imr_regional |>
    filter(!is.na(value)) |>
    arrange(desc(value)) |>
    mutate(Region_en = forcats::fct_reorder(Region_en, value)) |>
    ggplot(aes(x = value, y = Region_en,
               text = paste0("<b>Region:</b> ", Region_en, "<br>",
                             "<b>IMR:</b> ", round(value, 1)))) +
    # Aimags with gradient
    geom_col(data = ~ subset(., Type == "Aimag"), aes(fill = value), width = 0.7) +
    # Regions with distinct color
    geom_col(data = ~ subset(., Type == "Region"), fill = "#2c3e50", width = 0.7) +
    geom_text(aes(label = round(value, 1)), hjust = -0.2, color = "grey30", size = 3.5) +
    scale_fill_gradient2(
        low = "#27ae60",
        mid = "#f39c12",
        high = "#e74c3c",
        midpoint = mean(imr_regional$value[imr_regional$Type == "Aimag"])
    ) +
    geom_vline(
        xintercept = mean(imr_regional$value[imr_regional$Type == "Aimag"]),
        linetype = "dashed",
        color = "grey50",
        linewidth = 0.5
    ) +
    scale_x_continuous(expand = expansion(mult = c(0, 0.1))) +
    labs(
        title = "Infant Mortality by Aimag (2024 Average)",
        subtitle = "Dark bars represent Regional Averages",
        x = "Deaths per 1,000 live births",
        y = NULL
    ) +
    theme_minimal(base_size = 12) +
    theme(
        plot.title = element_text(face = "bold", size = 14),
        panel.grid.major.y = element_blank(),
        panel.grid.minor = element_blank(),
        axis.text.y = element_text(color = "black")
    )

plotly::ggplotly(p, tooltip = "text")

Adding Geographic Context

Combine with mapping for spatial analysis:

library(sf)

# Get aimag boundaries
aimags <- mn_boundaries(level = "ADM1")

# Join IMR data to map
imr_map <- aimags |>
    left_join(imr_regional, by = c("shapeName" = "Region_en"))

# Create choropleth
# Create choropleth
# Create choropleth
p <- imr_map |>
    ggplot() +
    geom_sf(aes(fill = value,
                text = paste0("<b>Region:</b> ", shapeName, "<br>",
                              "<b>IMR:</b> ", round(value, 1))),
            color = "white", size = 0.2) +
    scale_fill_viridis_c(
        option = "magma",
        direction = -1,
        name = "IMR\n(per 1,000)",
        labels = scales::label_number()
    ) +
    labs(
        title = "Infant Mortality Geography (2024 Average)",
        subtitle = "Spatial distribution of mortality rates",
        caption = "Source: NSO Mongolia"
    ) +
    theme_void() +
    theme(
        plot.title = element_text(face = "bold", size = 16),
        plot.subtitle = element_text(color = "grey40"),
        legend.position = "right",
        legend.title = element_text(size = 10, face = "bold")
    )

plotly::ggplotly(p, tooltip = "text") |>
    plotly::style(hoveron = "fills")

Key Functions Summary

Function	Purpose	Example
`nso_itms_search()`	Find tables by keyword	`nso_itms_search("mortality")`
`nso_table_meta()`	Get table dimensions	`nso_table_meta("DT_NSO_...")`
`nso_dim_values()`	List dimension values	`nso_dim_values(tbl, "Region")`
`nso_table_periods()`	Check time coverage	`nso_table_periods(tbl)`
`nso_data()`	Fetch data	`nso_data(tbl, selections, labels)`
`mn_boundaries()`	Get geographic boundaries	`mn_boundaries(level = "ADM1")`

Best Practices

Always use labels: Set labels = "en" in nso_data() for readable output
Check metadata first: Use nso_table_meta() to understand dimensions before fetching
Use appropriate selections: Specify dimensions by their English labels (e.g., "Total" not "0")
Filter carefully: Exclude total rows (usually code "0") when analyzing subgroups
Clean labels: Use trimws() to remove leading/trailing spaces from region names before joining

Indicator	Table_ID
Infant Mortality	DT_NSO_2100_015V1
Maternal Mortality	DT_NSO_2100_050V1
Under-5 Mortality	DT_NSO_2100_030V2
Cancer Incidence	DT_NSO_2100_012V1
TB Incidence	DT_NSO_2800_026V1
Communicable Diseases	DT_NSO_2100_020V2

Getting Started with mongolstats

Installation

Your First Analysis: Infant Mortality Trends

Step 1: Load Packages

Step 2: Find the Right Table

Step 3: Explore Table Metadata

Step 4: Fetch Data

Step 5: Visualize the Trend

Regional Comparison

Visualize Regional Disparities

Adding Geographic Context

Key Functions Summary

Best Practices

Common Workflows

Time Series Analysis

Regional Comparison

Spatial Epidemiology

Next Steps

Quick Reference: Common Health Tables